Tom Eichele

Tracking Whole-Brain Connectivity Dynamics in the Resting State

Spontaneous fluctuations are a hallmark of recordings of neural signals, emergent over time scales spanning milliseconds and tens of minutes. However, investigations of intrinsic brain organization based on resting-state functional magnetic resonance imaging have largely not taken into account the presence and potential of temporal variability, as most current approaches to examine functional connectivity (FC) implicitly assume that relationships are constant throughout the length of the recording. In this work, we describe an approach to assess whole-brain FC dynamics based on spatial independent component analysis, sliding time window correlation, and k-means clustering of windowed correlation matrices. The method is applied to resting-state data from a large sample (n = 405) of young adults. Our analysis of FC variability highlights particularly flexible connections between regions in lateral parietal and cingulate cortex, and argues against a labeling scheme where such regions are treated as separate and antagonistic entities. Additionally, clustering analysis reveals unanticipated FC states that in part diverge strongly from stationary connectivity patterns and challenge current descriptions of interactions between large-scale networks. Temporal trends in the occurrence of different FC states motivate theories regarding their functional roles and relationships with vigilance/arousal. Overall, we suggest that the study of time-varying aspects of FC can unveil flexibility in the functional coordination between different neural systems, and that the exploitation of these dynamics in further investigations may improve our understanding of behavioral shifts and adaptive processes.

Selective changes of resting-state networks in individuals at risk for Alzheimer's disease

Alzheimers disease (AD) is a neurodegenerative disorder that prominently affects cerebral connectivity. Assessing the functional connectivity at rest, recent functional MRI (fMRI) studies reported on the existence of resting-state networks (RSNs). RSNs are characterized by spatially coherent, spontaneous fluctuations in the blood oxygen level-dependent signal and are made up of regional patterns commonly involved in functions such as sensory, attention, or default mode processing. In AD, the default mode network (DMN) is affected by reduced functional connectivity and atrophy. In this work, we analyzed functional and structural MRI data from healthy elderly (n = 16) and patients with amnestic mild cognitive impairment (aMCI) (n = 24), a syndrome of high risk for developing AD. Two questions were addressed: (i) Are any RSNs altered in aMCI? (ii) Do changes in functional connectivity relate to possible structural changes? Independent component analysis of resting-state fMRI data identified eight spatially consistent RSNs. Only selected areas of the DMN and the executive attention network demonstrated reduced network-related activity in the patient group. Voxel-based morphometry revealed atrophy in both medial temporal lobes (MTL) of the patients. The functional connectivity between both hippocampi in the MTLs and the posterior cingulate of the DMN was present in healthy controls but absent in patients. We conclude that in individuals at risk for AD, a specific subset of RSNs is altered, likely representing effects of ongoing early neurodegeneration. We interpret our finding as a proof of principle, demonstrating that functional brain disorders can be characterized by functional-disconnectivity profiles of RSNs.

A Baseline for the Multivariate Comparison of Resting-State Networks

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.

Prediction of human errors by maladaptive changes in event-related brain networks

Humans engaged in monotonous tasks are susceptible to occasional errors that may lead to serious consequences, but little is known about brain activity patterns preceding errors. Using functional MRI and applying independent component analysis followed by deconvolution of hemodynamic responses, we studied error preceding brain activity on a trial-by-trial basis. We found a set of brain regions in which the temporal evolution of activation predicted performance errors. These maladaptive brain activity changes started to evolve ≈30 sec before the error. In particular, a coincident decrease of deactivation in default mode regions of the brain, together with a decline of activation in regions associated with maintaining task effort, raised the probability of future errors. Our findings provide insights into the brain network dynamics preceding human performance errors and suggest that monitoring of the identified precursor states may help in avoiding human errors in critical real-world situations.

Semi-automatic identification of independent components representing EEG artifact

OBJECTIVE Independent component analysis (ICA) can disentangle multi-channel electroencephalogram (EEG) signals into a number of artifacts and brain-related signals. However, the identification and interpretation of independent components is time-consuming and involves subjective decision making. We developed and evaluated a semi-automatic tool designed for clustering independent components from different subjects and/or EEG recordings. METHODS CORRMAP is an open-source EEGLAB plug-in, based on the correlation of ICA inverse weights, and finds independent components that are similar to a user-defined template. Component similarity is measured using a correlation procedure that selects components that pass a threshold. The threshold can be either user-defined or determined automatically. CORRMAP clustering performance was evaluated by comparing it with the performance of 11 users from different laboratories familiar with ICA. RESULTS For eye-related artifacts, a very high degree of overlap between users (phi>0.80), and between users and CORRMAP (phi>0.80) was observed. Lower degrees of association were found for heartbeat artifact components, between users (phi<0.70), and between users and CORRMAP (phi<0.65). CONCLUSIONS These results demonstrate that CORRMAP provides an efficient, convenient and objective way of clustering independent components. SIGNIFICANCE CORRMAP helps to efficiently use ICA for the removal EEG artifacts.

Functional brain networks in schizophrenia: a review

Functional magnetic resonance imaging (fMRI) has become a major technique for studying cognitive function and its disruption in mental illness, including schizophrenia. The major proportion of imaging studies focused primarily upon identifying regions which hemodynamic response amplitudes covary with particular stimuli and differentiate between patient and control groups. In addition to such amplitude based comparisons, one can estimate temporal correlations and compute maps of functional connectivity between regions which include the variance associated with event-related responses as well as intrinsic fluctuations of hemodynamic activity. Functional connectivity maps can be computed by correlating all voxels with a seed region when a spatial prior is available. An alternative are multivariate decompositions such as independent component analysis (ICA) which extract multiple components, each of which is a spatially distinct map of voxels with a common time course. Recent work has shown that these networks are pervasive in relaxed resting and during task performance and hence provide robust measures of intact and disturbed brain activity. This in turn bears the prospect of yielding biomarkers for schizophrenia, which can be described both in terms of disrupted local processing as well as altered global connectivity between large-scale networks. In this review we will summarize functional connectivity measures with a focus upon work with ICA and discuss the meaning of intrinsic fluctuations. In addition, examples of how brain networks have been used for classification of disease will be shown. We present work with functional network connectivity, an approach that enables the evaluation of the interplay between multiple networks and how they are affected in disease. We conclude by discussing new variants of ICA for extracting maximally group discriminative networks from data. In summary, it is clear that identification of brain networks and their inter-relationships with fMRI has great potential to improve our understanding of schizophrenia.

Posterior medial frontal cortex activity predicts post-error adaptations in task-related visual and motor areas.

As Seneca the Younger put it, “To err is human, but to persist is diabolical.” To prevent repetition of errors, human performance monitoring often triggers adaptations such as general slowing and/or attentional focusing. The posterior medial frontal cortex (pMFC) is assumed to monitor performance problems and to interact with other brain areas that implement the necessary adaptations. Whereas previous research showed interactions between pMFC and lateral-prefrontal regions, here we demonstrate that upon the occurrence of errors the pMFC selectively interacts with perceptual and motor regions and thereby drives attentional focusing toward task-relevant information and induces motor adaptation observed as post-error slowing. Functional magnetic resonance imaging data from an interference task reveal that error-related pMFC activity predicts the following: (1) subsequent activity enhancement in perceptual areas encoding task-relevant stimulus features; (2) activity suppression in perceptual areas encoding distracting stimulus features; and (3) post-error slowing-related activity decrease in the motor system. Additionally, diffusion-weighted imaging revealed a correlation of individual post-error slowing and white matter integrity beneath pMFC regions that are connected to the motor inhibition system, encompassing right inferior frontal gyrus and subthalamic nucleus. Thus, disturbances in task performance are remedied by functional interactions of the pMFC with multiple task-related brain regions beyond prefrontal cortex that result in a broad repertoire of adaptive processes at perceptual as well as motor levels.

Methods for Simultaneous EEG-fMRI: An Introductory Review

The simultaneous recording and analysis of electroencephalography (EEG) and fMRI data in human systems, cognitive and clinical neurosciences is rapidly evolving and has received substantial attention. The significance of multimodal brain imaging is documented by a steadily increasing number of laboratories now using simultaneous EEG-fMRI aiming to achieve both high temporal and spatial resolution of human brain function. Due to recent developments in technical and algorithmic instrumentation, the rate-limiting step in multimodal studies has shifted from data acquisition to analytic aspects. Here, we introduce and compare different methods for data integration and identify the benefits that come with each approach, guiding the reader toward an understanding and informed selection of the integration approach most suitable for addressing a particular research question.

Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study.

A key challenge in functional neuroimaging is the meaningful combination of results across subjects. Even in a sample of healthy participants, brain morphology and functional organization exhibit considerable variability, such that no two individuals have the same neural activation at the same location in response to the same stimulus. This inter-subject variability limits inferences at the group-level as average activation patterns may fail to represent the patterns seen in individuals. A promising approach to multi-subject analysis is group independent component analysis (GICA), which identifies group components and reconstructs activations at the individual level. GICA has gained considerable popularity, particularly in studies where temporal response models cannot be specified. However, a comprehensive understanding of the performance of GICA under realistic conditions of inter-subject variability is lacking. In this study we use simulated functional magnetic resonance imaging (fMRI) data to determine the capabilities and limitations of GICA under conditions of spatial, temporal, and amplitude variability. Simulations, generated with the SimTB toolbox, address questions that commonly arise in GICA studies, such as: (1) How well can individual subject activations be estimated and when will spatial variability preclude estimation? (2) Why does component splitting occur and how is it affected by model order? (3) How should we analyze component features to maximize sensitivity to intersubject differences? Overall, our results indicate an excellent capability of GICA to capture between-subject differences and we make a number of recommendations regarding analytic choices for application to functional imaging data.

Visual activation of auditory cortex reflects maladaptive plasticity in cochlear implant users

Cross-modal reorganization in the auditory cortex has been reported in deaf individuals. However, it is not well understood whether this compensatory reorganization induced by auditory deprivation recedes once the sensation of hearing is partially restored through a cochlear implant. The current study used electroencephalography source localization to examine cross-modal reorganization in the auditory cortex of post-lingually deafened cochlear implant users. We analysed visual-evoked potentials to parametrically modulated reversing chequerboard images between cochlear implant users (n = 11) and normal-hearing listeners (n = 11). The results revealed smaller P100 amplitudes and reduced visual cortex activation in cochlear implant users compared with normal-hearing listeners. At the P100 latency, cochlear implant users also showed activation in the right auditory cortex, which was inversely related to speech recognition ability with the cochlear implant. These results confirm a visual take-over in the auditory cortex of cochlear implant users. Incomplete reversal of this deafness-induced cortical reorganization might limit clinical benefit from a cochlear implant and help explain the high inter-subject variability in auditory speech comprehension.